Authors :
Rasheed. A. Shyyab; Seyedeh Hamideh Ebrahimi; Xinhai Lu
Volume/Issue :
Volume 8 - 2023, Issue 8 - August
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
http://tinyurl.com/42yfec9p
DOI :
https://doi.org/10.5281/zenodo.8315096
Abstract :
The goal of this research was to assess and
compare the differences in climate variables, specifically
precipitation (rainfall) and temperature maps, which are
crucial for many hydrological models. We utilized
ordinary kriging and multivariate interpolation
techniques to create maps of monthly and annual
rainfall in the Wadi Al-wala region of Jordan. This
involved collecting climate data from four rainfall
stations (Al-Muwaqqar, Jiza, Dhaba' Nursery, Muleih)
and the Er-Rabba metrological temperature station
between 1980 and 2012. The patterns of spatial variation
in the collected climate data were analyzed using
geostatistical methods to identify spatial variances and
predict the potential impacts of climate change on the
Wadi Al-Wala region. An experimental variogram of the
data was created and compared against three common
geostatistical models. Then spatial maps of the climate
variables were prepared through the kriging technique
by using the best-fit geostatistical model, which help
choose appropriate values of model parameters. Nugget-
to-sill ratio (<0.25) revealed that the surface water levels
have strong spatial dependence in the area. The
statistical indicators (RSS and r2) suggested that any of
the three geostatistical models, i.e., spherical, linear, and
exponential, can be selected as the best-fit model for
reliable and accurate spatial interpolation. However,
exponential and spherical models were used as the best-
fit models for the Al-Muwaqqar rainfall station with the
lower residual sum of squares (RSS= 1.038) and high
value of regression coefficient (r2= 0.192), and average
mean temperature from Er-Rabba station with higher
r2= 0.703 and RSS= 0.116 respectively, in the present
study.
Keywords :
Climate Variables, Wadi Al-Wala, Geostatics, Kriging Interpolation, Semivariance Analysis.
The goal of this research was to assess and
compare the differences in climate variables, specifically
precipitation (rainfall) and temperature maps, which are
crucial for many hydrological models. We utilized
ordinary kriging and multivariate interpolation
techniques to create maps of monthly and annual
rainfall in the Wadi Al-wala region of Jordan. This
involved collecting climate data from four rainfall
stations (Al-Muwaqqar, Jiza, Dhaba' Nursery, Muleih)
and the Er-Rabba metrological temperature station
between 1980 and 2012. The patterns of spatial variation
in the collected climate data were analyzed using
geostatistical methods to identify spatial variances and
predict the potential impacts of climate change on the
Wadi Al-Wala region. An experimental variogram of the
data was created and compared against three common
geostatistical models. Then spatial maps of the climate
variables were prepared through the kriging technique
by using the best-fit geostatistical model, which help
choose appropriate values of model parameters. Nugget-
to-sill ratio (<0.25) revealed that the surface water levels
have strong spatial dependence in the area. The
statistical indicators (RSS and r2) suggested that any of
the three geostatistical models, i.e., spherical, linear, and
exponential, can be selected as the best-fit model for
reliable and accurate spatial interpolation. However,
exponential and spherical models were used as the best-
fit models for the Al-Muwaqqar rainfall station with the
lower residual sum of squares (RSS= 1.038) and high
value of regression coefficient (r2= 0.192), and average
mean temperature from Er-Rabba station with higher
r2= 0.703 and RSS= 0.116 respectively, in the present
study.
Keywords :
Climate Variables, Wadi Al-Wala, Geostatics, Kriging Interpolation, Semivariance Analysis.
